How Cloud Storage Can Very Well Serve As Data Lake

by Dynamix Group Writer

A data lake offers a lot of flexibility to an organization, thereby enabling it to capture each aspect of business operations in the form of data. Therefore, with the division of computing and storage, it makes sense and is economical to store petabytes of data in a cloud data lake.  Post capturing and storing the data, multiple processing techniques can be applied to extract meaningful insights from the data. Although data warehousing has remained a standard approach to doing business analytics, it requires a fairly rigid schema for most types of data such as orders, order details, and inventory. The traditional data warehouse makes it difficult to deal with data that doesn’t conform to a well-defined schema as that data is discarded and lost forever. Shifting from data warehousing to a ‘store everything’ approach is advantageous only when insights from all the data can be extracted. Analysts, data scientists, and engineers often use only analytics tools of their choice for processing and analyzing data in the lake. Additionally, the data lake must back the ingestion of huge amounts of data from multiple sources.

Here’s how cloud storage is best suited for serving as the central storage repository:

 

Performance and durability: Cloud storage allows you to begin with small files and grow it to exabytes in size. Things such as high volume consumption of stored data, Pub/Sub, and high-volume ingestion of new data are well supported by cloud storage. Although performance is crucial for a data lake, durability is all the more important. Cloud storage ensures high annual durability. 

 

Consistency: One parameter that sets the cloud storage apart from other object stores is the support it extends to ensure robust consistency in scenarios such as listing buckets and objects, read-after-write operations, and granting access to resources. In the absence of consistency, you must execute complex, time-consuming workarounds for determining when data is available for processing. 

 

Cost efficiency: Cloud storage offers several storage cases at different prices, suiting different access patterns and availability requirements. It offers flexibility to balance the frequency and cost of data access. Data can be accessed from different storage classes without sacrificing performance by using a consistent API. 

 

Flexible processing: Cloud storage offers the option of native integration with many powerful Google Cloud services such as Dataflow for serverless analytics, BigQuery, Video Intelligence API, Dataproc ( Hadoop ecosystem), AI Platform, and Cloud Vision, thereby giving you the flexibility of choosing the right tool for analyzing your data. 

 

Central repository: By acting as a central repository for storing as well as accessing data across teams and departments, cloud storage assists in avoiding silos. It also ensures security by giving strong control capabilities and ensuring that your data doesn’t fall prey to the wrong hands. 

 

For big data consulting services and building data lakes, reputed companies such as Impetus Technologies are ideal. 

 

 

 

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Created on Nov 11th 2020 23:59. Viewed 256 times.

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